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Synthetic Data is the Future of AI.

Could you photograph someone from every angle, wearing every outfit, in every light? Synthetic data can account for infinite fluctuations, which would be impossible in real life. Imagine this for all your store's merchandise. Again, countless variants of one item would require hours of manual repositioning. Synthetic data improves machine learning models. Real-world data is random and does not cover all situations or events. Synthetic data can generate data at the edges or for unobserved conditions to mitigate this.
Gartner predicts that by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated.

Enabling a new wave of AI Innovation.

DigitSnap, powered by Digit7, is a synthetic data generating tool that can accelerate machine learning (ML) and artificial intelligence (AI) technologies as they expand throughout industries. Real-world data is erratic or sporadic and does not cover all situations or events. DigitSnap can generate data at the edges or for unseen conditions, and it can create faster and more economic datasets. DigitSnap addresses privacy Issues associated with sensitive real-world data. Offers you complete control over the data.
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Customers are able to make better purchases with the help of 3D product data models.

DigitSnap will be an important AI accelerator because it can be used in so many different ways in the real world. When there isn't enough data, AI can't be used because it is biased or can't handle situations that are rare or have never happened before. Data models need to be constantly improved and changed so that they stay stay unbiased and avoid degradation over time.  That means there is a constant need for fresh data  all the time. DigitSnap will help you solve this problem and give you a way to label your organization's data quickly and correctly.

Synthetic data can increase the accuracy of machine learning models. Real-world data is happenstance and does not contain all permutations of conditions or events possible in the real world. Synthetic data can counter this by generating data at the edges, or for conditions not yet seen.

The breadth of its applicability will make it a critical accelerator for AI. Synthetic data makes AI possible where lack of data makes AI unusable due to bias or inability to recognize rare or unprecedented scenarios.